Model Serving and Inference

Goal

Serve models against explicit quality, throughput, latency, availability, privacy, and cost requirements. Optimize measured bottlenecks rather than assuming one serving stack fits every model.

Workflow

1. Specify workload. Estimate request rates, prompt/output token distributions, concurrency, context lengths, streaming needs, SLA, model size, and hardware constraints.
2. Establish baseline. Measure time to first token, inter-token latency, end-to-end p50/p95/p99, throughput, GPU/CPU/memory utilization, error rate, and cost per successful request.
3. Select deployment mode. Compare hosted APIs, dedicated endpoints, self-hosted runtimes, and edge/local inference based on control, scale, data residency, and operational capacity.
4. Tune safely. Evaluate batching, continuous batching, KV-cache behavior, quantization, speculative decoding, prefix caching, and request limits against representative prompts and outputs.
5. Preserve quality. Run task-level and safety evaluations after changing precision, model version, tokenizer, sampling, or serving configuration.
6. Protect capacity. Set concurrency limits, queue bounds, timeouts, cancellation, backpressure, and rate limits. Return useful overload errors instead of unbounded queuing.
7. Handle failures. Use bounded retries with jitter for transient failures, idempotency for retried actions, and carefully evaluated fallbacks. Do not retry permanent invalid requests indefinitely.
8. Roll out gradually. Use canary traffic, shadow comparisons where safe, health checks, rollback criteria, and pinned model/runtime versions.
9. Monitor drift and cost. Watch latency, throughput, token distribution, quality, hardware saturation, and provider changes.

Performance dimensions

• Time to first token (TTFT) and inter-token latency for streaming.
• End-to-end latency and successful tasks per second.
• Tokens per second and cost per successful task.
• Memory headroom, queue time, cache hit rate, and failure/retry rates.

Avoid

• Reporting throughput without concurrency and workload details.
• Comparing systems with different prompt/output lengths or quality targets.
• Enabling cache reuse across users or tenants when context isolation cannot be guaranteed.
• Using quantization or caching without regression tests and security review.
• Treating a healthy process as proof the model endpoint is serving correct results.

Deliverable

Document workload assumptions, benchmark methodology, runtime/model versions, capacity limits, rollout/rollback plan, quality impact, and operating metrics.

References

• vLLM documentation: https://docs.vllm.ai/
• Hugging Face Text Generation Inference: https://huggingface.co/docs/text-generation-inference/index
• NVIDIA TensorRT-LLM: https://nvidia.github.io/TensorRT-LLM/